knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(tidyverse)
library(here)
library(sf)
library(tmap)
### install.packages("tmap")
### update.packages(ask = FALSE)
sf_trees <- read_csv(here("data", "sf_trees", "sf_trees.csv"))
Example 1: Find counts of observations by legal_status & wrangle a bit
### method 1: group_by() %>% summarize()
sf_trees %>%
group_by(legal_status) %>%
summarize(tree_count = n())
## # A tibble: 10 × 2
## legal_status tree_count
## <chr> <int>
## 1 DPW Maintained 141725
## 2 Landmark tree 42
## 3 Permitted Site 39732
## 4 Planning Code 138.1 required 971
## 5 Private 163
## 6 Property Tree 316
## 7 Section 143 230
## 8 Significant Tree 1648
## 9 Undocumented 8106
## 10 <NA> 54
### method 2: different way plus a few new functions
top_5_status <- sf_trees %>%
count(legal_status) %>%
drop_na(legal_status) %>%
rename(tree_count = n) %>%
relocate(tree_count) %>% # easily re-order columns, this will relocate to front of my data
slice_max(tree_count, n = 5) %>%
arrange(-tree_count) #- means arrange by descending, no - arrange by ascending or (desc(tree_count))
Make a graph of the top 5 from above
ggplot(data = top_5_status, aes(x = fct_reorder(legal_status, tree_count), y = tree_count)) +
geom_col(fill = "darkgreen") +
labs(x = "Legal Status", y = "Tree Count") +
coord_flip() +
theme_minimal()
Example 2: Only keep observations where legal status is “Permitted Site” and caretaker is “MTA”, and store as permitted_data_df
shift-control-c to comment/uncoment quickly (active or inactive code)
# sf_trees$legal_status %>% unique()
# unique(sf_trees$caretaker)
permitted_data_df <- sf_trees %>%
filter(legal_status == c("Permitted Site") & caretaker == "MTA")
# filter(legal_status %in% c("Permitted Site", "Private")
Example 3: Only keep Blackwood Acacia trees, and then only keep columns legal_status, date, latitude, longitude and store as blackwood_acacia_df
blackwood_acacia_df <- sf_trees %>%
filter(str_detect(species, "Blackwood Acacia")) %>%
select(legal_status, date, lat = latitude, lon = longitude)
### Make a little graph of locations
ggplot(data = blackwood_acacia_df, aes(x = lon, y = lat)) +
geom_point(color = "darkgreen")
Example 4: Use tidyr::seperate() take on column and seperate it out
sf_trees_sep <- sf_trees %>%
separate(species, into = c('spp_scientific', 'spp_common'), sep = ' :: ')
Example 5: use ‘tidyr::unite()’
ex_5 <- sf_trees %>%
unite('id_status', tree_id, legal_status, sep = '_COOL_')
Step 1: convert lat/lon to spatial point, st_as_sf()
blackwood_accacia_sf <- blackwood_acacia_df %>%
drop_na(lat, lon) %>%
st_as_sf(coords = c('lon', 'lat'))
### we need to tell R what the coordinate refernce system is
st_crs(blackwood_accacia_sf) <- 4326
ggplot(data = blackwood_accacia_sf) +
geom_sf(color = 'darkgreen')+
theme_minimal()
Read in the SF shapefile and add to map
sf_map <- read_sf(here('data', 'sf_map', 'tl_2017_06075_roads.shp'))
sf_map_transform <- st_transform(sf_map, 4326)
ggplot(data = sf_map_transform) +
geom_sf()
Combine the maps!
ggplot() +
geom_sf(data = sf_map,
size = .1,
color = 'darkgrey') +
geom_sf(data = blackwood_accacia_sf,
color = 'red',
size = 0.5) +
theme_void() +
labs(title = 'Blackwood acacias in SF')
tmap_mode('view')
tm_shape(blackwood_accacia_sf) +
tm_dots()